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Journal articles on the topic 'Subcellular Localization'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Han, Guo-Sheng, and Zu-Guo Yu. "ML-rRBF-ECOC: A Multi-Label Learning Classifier for Predicting Protein Subcellular Localization with Both Single and Multiple Sites." Current Proteomics 16, no. 5 (2019): 359–65. http://dx.doi.org/10.2174/1570164616666190103143945.

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Background: The subcellular localization of a protein is closely related with its functions and interactions. More and more evidences show that proteins may simultaneously exist at, or move between, two or more different subcellular localizations. Therefore, predicting protein subcellular localization is an important but challenging problem. Observation: Most of the existing methods for predicting protein subcellular localization assume that a protein locates at a single site. Although a few methods have been proposed to deal with proteins with multiple sites, correlations between subcellular

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Wu, Ze Yue, and Yue Hui Chen. "Predicting Protein Subcellular Localization Using the Algorithm of Diversity Finite Coefficient Combined with Artificial Neural Network." Advanced Materials Research 756-759 (September 2013): 3760–65. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3760.

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Protein subcellular localization is an important research field of bioinformatics. The subcellular localization of proteins classification problem is transformed into several two classification problems with error-correcting output codes. In this paper, we use the algorithm of the increment of diversity combined with artificial neural network to predict protein in SNL6 which has six subcelluar localizations. The prediction ability was evaluated by 5-jackknife cross-validation. Its predicted result is 81.3%. By com-paring its results with other methods, it indicates the new approach is feasible

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Zhang, Yu-Hang, ShiJian Ding, Lei Chen, Tao Huang, and Yu-Dong Cai. "Subcellular Localization Prediction of Human Proteins Using Multifeature Selection Methods." BioMed Research International 2022 (September 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/3288527.

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Subcellular localization attempts to assign proteins to one of the cell compartments that performs specific biological functions. Finding the link between proteins, biological functions, and subcellular localization is an effective way to investigate the general organization of living cells in a systematic manner. However, determining the subcellular localization of proteins by traditional experimental approaches is difficult. Here, protein–protein interaction networks, functional enrichment on gene ontology and pathway, and a set of proteins having confirmed subcellular localization were appl

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Wang, Xiao, Lixiang Yang, and Rong Wang. "DRpred: A Novel Deep Learning-Based Predictor for Multi-Label mRNA Subcellular Localization Prediction by Incorporating Bayesian Inferred Prior Label Relationships." Biomolecules 14, no. 9 (2024): 1067. http://dx.doi.org/10.3390/biom14091067.

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The subcellular localization of messenger RNA (mRNA) not only helps us to understand the localization regulation of gene expression but also helps to understand the relationship between RNA localization pattern and human disease mechanism, which has profound biological and medical significance. Several predictors have been proposed for predicting the subcellular localization of mRNA. However, there is still considerable room for improvement in their predictive performance, especially regarding multi-label prediction. This study proposes a novel multi-label predictor, DRpred, for mRNA subcellul

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Aßfalg, Johannes, Jing Gong, Hans-Peter Kriegel, Alexey Pryakhin, Tiandi Wei, and Arthur Zimek. "Investigating a Correlation between Subcellular Localization and Fold of Proteins." JUCS - Journal of Universal Computer Science 16, no. (5) (2010): 604–21. https://doi.org/10.3217/jucs-016-05-0604.

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When considering the prediction of a structural class for a protein as a classificationproblem, usually a classifier is based on a feature vector x ∊ ℝn, where the features represent certain attributes of the primary sequence or derived properties (e.g., the predicted secondary structure) of a given protein. Since the structure of a protein (i.e., its native conformation) is stable only under specific environmental conditions, it is commonly accepted to assume proteins being evolutionarily adapted to specific subcellular localizations and according to their physicochemical environment. Our sta

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6

Wu, Ze Yue, and Yue Hui Chen. "Predicting Protein Subcellular Localization Using the Algorithm of Increment of Diversity Combined with Weighted K-Nearest Neighbor." Advanced Materials Research 765-767 (September 2013): 3099–103. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.3099.

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Protein subcellular localization is an important research field of bioinformatics. In this paper, we use the algorithm of the increment of diversity combined with weighted K nearest neighbor to predict protein in SNL6 which has six subcelluar localizations and SNL9 which has nine subcelluar localizations. We use the increment of diversity to extract diversity finite coefficient as new features of proteins. And the basic classifier is weighted K-nearest neighbor. The prediction ability was evaluated by 5-jackknife cross-validation. Its predicted result is 83.3% for SNL6 and 87.6 % for SNL9. By

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7

Lin, Yang, Xiaoyong Pan, and Hong-Bin Shen. "lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning." Bioinformatics 37, no. 16 (2021): 2308–16. http://dx.doi.org/10.1093/bioinformatics/btab127.

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Abstract Motivation Long non-coding RNAs (lncRNAs) are generally expressed in a tissue-specific way, and subcellular localizations of lncRNAs depend on the tissues or cell lines that they are expressed. Previous computational methods for predicting subcellular localizations of lncRNAs do not take this characteristic into account, they train a unified machine learning model for pooled lncRNAs from all available cell lines. It is of importance to develop a cell-line-specific computational method to predict lncRNA locations in different cell lines. Results In this study, we present an updated cel

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8

Luo, Haiwei. "Predicted Protein Subcellular Localization in Dominant Surface Ocean Bacterioplankton." Applied and Environmental Microbiology 78, no. 18 (2012): 6550–57. http://dx.doi.org/10.1128/aem.01406-12.

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ABSTRACTBacteria consume dissolved organic matter (DOM) through hydrolysis, transport and intracellular metabolism, and these activities occur in distinct subcellular localizations. Bacterial protein subcellular localizations for several major marine bacterial groups were predicted using genomic, metagenomic and metatranscriptomic data sets following modification of MetaP software for use with partial gene sequences. The most distinct pattern of subcellular localization was found forBacteroidetes, whose genomes were substantially enriched with outer membrane and extracellular proteins but depl

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9

Hao, Zhiming, Xiaohua Li, Taidong Qiao, Rui Du, Guoyun Zhang, and Daiming Fan. "Subcellular Localization of CIAPIN1." Journal of Histochemistry & Cytochemistry 54, no. 12 (2006): 1437–44. http://dx.doi.org/10.1369/jhc.6a6960.2006.

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10

Anastasio, A., AL Carillo, M. Ciccareli, B. Trimarco, G. Iaccarino, and D. Sorriento. "P440Targeting subcellular GRK2 localization." Cardiovascular Research 103, suppl 1 (2014): S81.2—S81. http://dx.doi.org/10.1093/cvr/cvu091.119.

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11

Ranly, D. M., L. Amstutz, and D. Horn. "Subcellular localization of glutaraldehyde." Dental Traumatology 6, no. 6 (1990): 251–54. http://dx.doi.org/10.1111/j.1600-9657.1990.tb00427.x.

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12

Suzuki, Hiroshi I., Akihiro Katsura, and Kohei Miyazono. "Subcellular localization of Lin28A." Cancer Science 106, no. 9 (2015): September cover. http://dx.doi.org/10.1111/cas.12750.

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13

Mofatteh, Mohammad, and Simon L. Bullock. "SnapShot: Subcellular mRNA Localization." Cell 169, no. 1 (2017): 178–178. http://dx.doi.org/10.1016/j.cell.2017.03.004.

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14

Scott, Michelle S., Sara J. Calafell, David Y. Thomas, and Michael T. Hallett. "Refining Protein Subcellular Localization." PLoS Computational Biology 1, no. 6 (2005): e66. http://dx.doi.org/10.1371/journal.pcbi.0010066.

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15

SOMMER, BJÖRN, BENJAMIN KORMEIER, PAVEL S. DEMENKOV, et al. "SUBCELLULAR LOCALIZATION CHARTS: A NEW VISUAL METHODOLOGY FOR THE SEMI-AUTOMATIC LOCALIZATION OF PROTEIN-RELATED DATA SETS." Journal of Bioinformatics and Computational Biology 11, no. 01 (2013): 1340005. http://dx.doi.org/10.1142/s0219720013400052.

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The CELLmicrocosmos PathwayIntegration (CmPI) was developed to support and visualize the subcellular localization prediction of protein-related data such as protein-interaction networks. From the start it was possible to manually analyze the localizations by using an interactive table. It was, however, quite complicated to compare and analyze the different localization results derived from data integration as well as text-mining-based databases. The current software release provides a new interactive visual workflow, the Subcellular Localization Charts. As an application case, a MUPP1-related

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16

Yang, Xiao-Fei, Yuan-Ke Zhou, Lin Zhang, Yang Gao, and Pu-Feng Du. "Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions." Current Bioinformatics 15, no. 6 (2020): 554–62. http://dx.doi.org/10.2174/1574893614666190902151038.

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Background: Long non-coding RNAs (lncRNAs) are transcripts with a length more than 200 nucleotides, functioning in the regulation of gene expression. More evidence has shown that the biological functions of lncRNAs are intimately related to their subcellular localizations. Therefore, it is very important to confirm the lncRNA subcellular localization. Methods: In this paper, we proposed a novel method to predict the subcellular localization of lncRNAs. To more comprehensively utilize lncRNA sequence information, we exploited both kmer nucleotide composition and sequence order correlated factor

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17

Li, Bo, Lijun Cai, Bo Liao, Xiangzheng Fu, Pingping Bing, and Jialiang Yang. "Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features." Molecules 24, no. 5 (2019): 919. http://dx.doi.org/10.3390/molecules24050919.

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The prediction of protein subcellular localization is critical for inferring protein functions, gene regulations and protein-protein interactions. With the advances of high-throughput sequencing technologies and proteomic methods, the protein sequences of numerous yeasts have become publicly available, which enables us to computationally predict yeast protein subcellular localization. However, widely-used protein sequence representation techniques, such as amino acid composition and the Chou’s pseudo amino acid composition (PseAAC), are difficult in extracting adequate information about the in

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18

Nair, Rajesh, and Burkhard Rost. "Sequence conserved for subcellular localization." Protein Science 11, no. 12 (2009): 2836–47. http://dx.doi.org/10.1110/ps.0207402.

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19

Rudner, D. Z., and R. Losick. "Protein Subcellular Localization in Bacteria." Cold Spring Harbor Perspectives in Biology 2, no. 4 (2010): a000307. http://dx.doi.org/10.1101/cshperspect.a000307.

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20

Gillard, Baiba K., Lisa T. Thurmon, and Donald M. Marcus. "Variable subcellular localization of glycosphingolipids." Glycobiology 3, no. 1 (1993): 57–67. http://dx.doi.org/10.1093/glycob/3.1.57.

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21

Cheuk, W., and John K. C. Chan. "Subcellular Localization of Immunohistochemical Signals." International Journal of Surgical Pathology 12, no. 3 (2004): 185–206. http://dx.doi.org/10.1177/106689690401200301.

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22

Villadangos, Leticia, and Juan M. Serrador. "Subcellular Localization Guides eNOS Function." International Journal of Molecular Sciences 25, no. 24 (2024): 13402. https://doi.org/10.3390/ijms252413402.

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Nitric oxide synthases (NOS) are enzymes responsible for the cellular production of nitric oxide (NO), a highly reactive signaling molecule involved in important physiological and pathological processes. Given its remarkable capacity to diffuse across membranes, NO cannot be stored inside cells and thus requires multiple controlling mechanisms to regulate its biological functions. In particular, the regulation of endothelial nitric oxide synthase (eNOS) activity has been shown to be crucial in vascular homeostasis, primarily affecting cardiovascular disease and other pathophysiological process

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23

MÃ©rit, Xavier, Jacques Frot-Coutaz, RenÃ© Got, and Robert LÃ©toublon. "Aspergillus nigerG proteins: subcellular localization." FEMS Microbiology Letters 92, no. 3 (1992): 259–63. http://dx.doi.org/10.1111/j.1574-6968.1992.tb05271.x.

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24

MIRANDA, MARIANA R., LEÓN A. BOUVIER, GASPAR E. CANEPA, and CLAUDIO A. PEREIRA. "Subcellular localization ofTrypanosoma cruziarginine kinase." Parasitology 136, no. 10 (2009): 1201–7. http://dx.doi.org/10.1017/s0031182009990448.

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SUMMARYPhosphoarginine is a cell energy buffer molecule synthesized by the enzyme arginine kinase. InTrypanosoma cruzi, the aetiological agent of Chagas' disease, 2 different isoforms were identified by data mining, but only 1 was expressed during the parasite life cycle. The digitonin extraction pattern of arginine kinase differed from those obtained for reservosomes, glycosomes and mitochondrial markers, and similar to the cytosolic marker. Immunofluorescence analysis revealed that although arginine kinase is localized mainly in unknown punctuated structures and also in the cytosol, it did n

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25

Seiji, M., K. Shimao, M. S. C. Birbeck, and T. B. Fitzpatrick. "SUBCELLULAR LOCALIZATION OF MELANIN BIOSYNTHESTS." Annals of the New York Academy of Sciences 100, no. 2 (2006): 497–533. http://dx.doi.org/10.1111/j.1749-6632.1963.tb42911.x.

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26

Kloc, Malgorzata, N. Ruth Zearfoss, and Laurence D. Etkin. "Mechanisms of Subcellular mRNA Localization." Cell 108, no. 4 (2002): 533–44. http://dx.doi.org/10.1016/s0092-8674(02)00651-7.

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27

Bononi, Angela, and Paolo Pinton. "Study of PTEN subcellular localization." Methods 77-78 (May 2015): 92–103. http://dx.doi.org/10.1016/j.ymeth.2014.10.002.

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28

Monaco, Marie E., Nicholas D. Cassai, and Gurdip S. Sidhu. "Subcellular localization of phosphatidylinositol synthesis." Biochemical and Biophysical Research Communications 348, no. 3 (2006): 1200–1204. http://dx.doi.org/10.1016/j.bbrc.2006.07.196.

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29

Van Kuilenburg, A. B. P., H. Van Lenthe, R. J. A. Wanders, and A. H. Van Gennip. "Subcellular localization of dihydropyrimidine dehydrogenase." Clinical Biochemistry 30, no. 3 (1997): 290–91. http://dx.doi.org/10.1016/s0009-9120(97)87830-7.

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30

Tajeddine, Nicolas, Nadège Zanou, Monique Van Schoor, Jean Lebacq, and Philippe Gailly. "TRPC1: Subcellular Localization?: Fig. 1." Journal of Biological Chemistry 285, no. 5 (2010): le1. http://dx.doi.org/10.1074/jbc.l109.073221.

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31

Pierleoni, A., P. L. Martelli, P. Fariselli, and R. Casadio. "eSLDB: eukaryotic subcellular localization database." Nucleic Acids Research 35, Database (2007): D208—D212. http://dx.doi.org/10.1093/nar/gkl775.

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32

Zhang, Yizhe, Alden Moss, Kristine Tan, and Amy E. Herr. "Barcodes for subcellular protein localization." Nature Biomedical Engineering 3, no. 9 (2019): 673–75. http://dx.doi.org/10.1038/s41551-019-0430-3.

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33

Yang, Yanbo, Minhyoung Lee, and Gregory D. Fairn. "Phospholipid subcellular localization and dynamics." Journal of Biological Chemistry 293, no. 17 (2018): 6230–40. http://dx.doi.org/10.1074/jbc.r117.000582.

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S�ez, Doris E., and Juan C. Slebe. "Subcellular localization of aldolase B." Journal of Cellular Biochemistry 78, no. 1 (2000): 62–72. http://dx.doi.org/10.1002/(sici)1097-4644(20000701)78:1<62::aid-jcb6>3.0.co;2-w.

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35

Yu, Chin-Sheng, Yu-Ching Chen, Chih-Hao Lu, and Jenn-Kang Hwang. "Prediction of protein subcellular localization." Proteins: Structure, Function, and Bioinformatics 64, no. 3 (2006): 643–51. http://dx.doi.org/10.1002/prot.21018.

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36

Wang, Xiao, Sujun Wang, Rong Wang, and Xu Gao. "PreSubLncR: Predicting Subcellular Localization of Long Non-Coding RNA Based on Multi-Scale Attention Convolutional Network and Bidirectional Long Short-Term Memory Network." Processes 12, no. 4 (2024): 666. http://dx.doi.org/10.3390/pr12040666.

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The subcellular localization of long non-coding RNA (lncRNA) provides important insights and opportunities for an in-depth understanding of cell biology, revealing disease mechanisms, drug development, and innovation in the biomedical field. Although several computational methods have been proposed to identify the subcellular localization of lncRNA, it is difficult to accurately predict the subcellular localization of lncRNA effectively with these methods. In this study, a new deep-learning predictor called PreSubLncR has been proposed for accurately predicting the subcellular localization of

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Palmer, Ella, and Tom Freeman. "Investigation Into the use of C- and N-terminal GFP Fusion Proteins for Subcellular Localization Studies Using Reverse Transfection Microarrays." Comparative and Functional Genomics 5, no. 4 (2004): 342–53. http://dx.doi.org/10.1002/cfg.405.

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Reverse transfection microarrays were described recently as a high throughput method for studying gene function. We have investigated the use of this technology for determining the subcellular localization of proteins. Genes encoding 16 proteins with a variety of functions were placed in Gateway expression constructs with 3′ or 5′ green fluorescent protein (GFP) tags. These were then packaged in transfection reagent and spotted robotically onto a glass slide to form a reverse transfection array. HEK293T cells were grown over the surface of the array until confluent and GFP fluorescence visuali

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Shen, Yinan, Yijie Ding, Jijun Tang, Quan Zou, and Fei Guo. "Critical evaluation of web-based prediction tools for human protein subcellular localization." Briefings in Bioinformatics 21, no. 5 (2019): 1628–40. http://dx.doi.org/10.1093/bib/bbz106.

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Abstract Human protein subcellular localization has an important research value in biological processes, also in elucidating protein functions and identifying drug targets. Over the past decade, a number of protein subcellular localization prediction tools have been designed and made freely available online. The purpose of this paper is to summarize the progress of research on the subcellular localization of human proteins in recent years, including commonly used data sets proposed by the predecessors and the performance of all selected prediction tools against the same benchmark data set. We

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Shalini kaushik, Usha Chouhan, and Ashok Dwivedi. "Prediction of protein subcellular localization of human protein using j48, random forest and best first tree techniques." JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH 1, no. 12 (2021): 17–30. http://dx.doi.org/10.46947/joaasr112201791.

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Functional explication of unascertained proteins is a remarkable achievement in proteomics. Proteins subcellular localization serves as the key annotation. Many prediction techniques were developed emphasizing on an individual biological point or speculating a subset of all localizations. Emulating the protein localization that is studied pivotal is carried out by gathering all the necessary biological relevant information and addressing the necessity of improving the prediction accuracy. Proteins carry an obligatory role in a wide range of bioprocess such as catalysis of biochemical reaction,

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Abdrabou, Abdalla, Daniel Brandwein, and Zhixiang Wang. "Differential Subcellular Distribution and Translocation of Seven 14-3-3 Isoforms in Response to EGF and During the Cell Cycle." International Journal of Molecular Sciences 21, no. 1 (2020): 318. http://dx.doi.org/10.3390/ijms21010318.

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Multiple isoforms of 14-3-3 proteins exist in different organisms. In mammalian cells, 14-3-3 protein has seven isoforms (α/β, ε, η, γ, σ, θ/τ, and δ/ζ), with α and δ representing the phosphorylated versions of β and ζ, respectively. While the existence of multiple isoforms may represent one more level of regulation in 14-3-3 signaling, our knowledge regarding the isoform-specific functions of 14-3-3 proteins is very limited. Determination of the subcellular localization of the different 14-3-3 isoforms could give us important clues of their specific functions. In this study, by using indirect

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Semwal, Rahul, and Pritish Kumar Varadwaj. "HumDLoc: Human Protein Subcellular Localization Prediction Using Deep Neural Network." Current Genomics 21, no. 7 (2020): 546–57. http://dx.doi.org/10.2174/1389202921999200528160534.

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Aims: To develop a tool that can annotate subcellular localization of human proteins. Background: With the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/ compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be

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Wang, Shihang, Zhehan Shen, Taigang Liu, Wei Long, Linhua Jiang, and Sihua Peng. "DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning." Molecules 28, no. 5 (2023): 2284. http://dx.doi.org/10.3390/molecules28052284.

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The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA’s subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a V

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Nielsen, Joachim, and Niels Ørtenblad. "Physiological aspects of the subcellular localization of glycogen in skeletal muscle." Applied Physiology, Nutrition, and Metabolism 38, no. 2 (2013): 91–99. http://dx.doi.org/10.1139/apnm-2012-0184.

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Glucose is stored in skeletal muscle fibers as glycogen, a branched-chain polymer observed in electron microscopy images as roughly spherical particles (known as β-particles of 10–45 nm in diameter), which are distributed in distinct localizations within the myofibers and are physically associated with metabolic and scaffolding proteins. Although the subcellular localization of glycogen has been recognized for more than 40 years, the physiological role of the distinct localizations has received sparse attention. Recently, however, studies involving stereological, unbiased, quantitative methods

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Jie, Minwen, Tong Feng, Wei Huang, et al. "Subcellular Localization of miRNAs and Implications in Cellular Homeostasis." Genes 12, no. 6 (2021): 856. http://dx.doi.org/10.3390/genes12060856.

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MicroRNAs (miRNAs) are thought to act as post-transcriptional regulators in the cytoplasm by either dampening translation or stimulating degradation of target mRNAs. With the increasing resolution and scope of RNA mapping, recent studies have revealed novel insights into the subcellular localization of miRNAs. Based on miRNA subcellular localization, unconventional functions and mechanisms at the transcriptional and post-transcriptional levels have been identified. This minireview provides an overview of the subcellular localization of miRNAs and the mechanisms by which they regulate transcrip

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Zeng, Chao, and Michiaki Hamada. "RNA-Seq Analysis Reveals Localization-Associated Alternative Splicing across 13 Cell Lines." Genes 11, no. 7 (2020): 820. http://dx.doi.org/10.3390/genes11070820.

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Alternative splicing, a ubiquitous phenomenon in eukaryotes, is a regulatory mechanism for the biological diversity of individual genes. Most studies have focused on the effects of alternative splicing for protein synthesis. However, the transcriptome-wide influence of alternative splicing on RNA subcellular localization has rarely been studied. By analyzing RNA-seq data obtained from subcellular fractions across 13 human cell lines, we identified 8720 switching genes between the cytoplasm and the nucleus. Consistent with previous reports, intron retention was observed to be enriched in the nu

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Chen, Xingjian, Xuejiao Hu, Wenxin Yi, Xiang Zou, and Wei Xue. "Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach." BioMed Research International 2019 (January 30, 2019): 1–9. http://dx.doi.org/10.1155/2019/2436924.

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The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apopt

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Lyu, Jianyi, Peijie Zheng, Yue Qi, and Guohua Huang. "LightGBM-LncLoc: A LightGBM-Based Computational Predictor for Recognizing Long Non-Coding RNA Subcellular Localization." Mathematics 11, no. 3 (2023): 602. http://dx.doi.org/10.3390/math11030602.

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Long non-coding RNAs (lncRNA) are a class of RNA transcripts with more than 200 nucleotide residues. LncRNAs play versatile roles in cellular processes and are thus becoming a hot topic in the field of biomedicine. The function of lncRNAs was discovered to be closely associated with subcellular localization. Although many methods have been developed to identify the subcellular localization of lncRNAs, there still is much room for improvement. Herein, we present a lightGBM-based computational predictor for recognizing lncRNA subcellular localization, which is called LightGBM-LncLoc. LightGBM-Ln

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Kamaruddin, Muhammad Izzat, Rohayanti Hassan, Nabil Rayhan, and Muhammad Luqman Mohd-Shafie. "Classifying Virus Strain Using a Machine Learning Model Based on Subcellular Localization Data." International Journal of Innovative Computing 14, no. 1 (2024): 7–13. http://dx.doi.org/10.11113/ijic.v14n1.460.

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The topic of mRNA subcellular localization is very useful for further study. And one of the most significant reasons to study deep into this topic is to study mRNA functions. The location of the particular mRNA is very important, as well as its function. Localization of mRNA can be used for a variety of reasons. Therefore, several tools were developed to predict mRNA localization. Due to the various importance and functions of subcellular localization, further studies and research have been given significant attention by the researchers. Among all of the tools developed, some notable differenc

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Yannoni, Y. M., and K. White. "Domain necessary for Drosophila ELAV nuclear localization: function requires nuclear ELAV." Journal of Cell Science 112, no. 24 (1999): 4501–12. http://dx.doi.org/10.1242/jcs.112.24.4501.

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The neuron specific Drosophila ELAV protein belongs to the ELAV family of RNA binding proteins which are characterized by three highly conserved RNA recognition motifs, an N-terminal domain, and a hinge region between the second and third RNA recognition motifs. Despite their highly conserved RNA recognition motifs the ELAV family members are a group of proteins with diverse posttranscriptional functions including splicing regulation, mRNA stability and translatability and have a variety of subcellular localizations. The role of the ELAV hinge in localization and function was examined using tr

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Solís-Fernández, Guillermo, Ana Montero-Calle, Javier Martínez-Useros, et al. "Spatial Proteomic Analysis of Isogenic Metastatic Colorectal Cancer Cells Reveals Key Dysregulated Proteins Associated with Lymph Node, Liver, and Lung Metastasis." Cells 11, no. 3 (2022): 447. http://dx.doi.org/10.3390/cells11030447.

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Metastasis is the primary cause of colorectal cancer (CRC) death. The liver and lung, besides adjacent lymph nodes, are the most common sites of metastasis. Here, we aimed to study the lymph nodes, liver, and lung CRC metastasis by quantitative spatial proteomics analysis using CRC cell-based models that recapitulate these metastases. The isogenic KM12 cell system composed of the non-metastatic KM12C cells, liver metastatic KM12SM cells, and liver and lung metastatic KM12L4a cells, and the isogenic non-metastatic SW480 and lymph nodes metastatic SW620 cells, were used. Cells were fractionated

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